commit 7e0585421ff1aa74780e54a1550b5326bc06d4cc
Author: 王宏建 <macrocc@outlook.com>
Date:   Wed Jul 1 10:56:43 2026 +0800

    feat(inference): 添加模型加载和分词器功能
    feat(model): 实现 Llama 模型及其解码器层
    feat(norm): 添加 RMSNorm 归一化层
    feat(rope): 实现 RoPE 位置编码

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diff --git a/Cargo.toml b/Cargo.toml
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index 0000000..d98b4bf
--- /dev/null
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+[package]
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+version = "0.1.0"
+edition = "2021"
+
+[lib]
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+path = "src/lib.rs"
+
+[[bin]]
+name = "minicpm-burn"
+path = "src/main.rs"
+
+[dependencies]
+burn = { version = "0.21", features = ["std", "wgpu"] }
+tokenizers = "0.20"
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+anyhow = "1.0"
+rand = "0.8"
+memmap2 = "0.9"
+clap = { version = "4.5", features = ["derive"] }
+
+[dev-dependencies]
diff --git a/MiniCPM5-1B/.gitattributes b/MiniCPM5-1B/.gitattributes
new file mode 100644
index 0000000..78e4a15
--- /dev/null
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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diff --git a/MiniCPM5-1B/README-cn.md b/MiniCPM5-1B/README-cn.md
new file mode 100644
index 0000000..0cf6073
--- /dev/null
+++ b/MiniCPM5-1B/README-cn.md
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+---
+license: apache-2.0
+language:
+  - zh
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - minicpm
+  - minicpm5
+  - llama
+  - text-generation
+  - long-context
+  - tool-calling
+  - on-device
+  - edge-ai
+datasets:
+  - openbmb/Ultra-FineWeb
+  - openbmb/Ultra-FineWeb-L3
+  - openbmb/UltraData-Math
+  - openbmb/UltraData-SFT-2605
+---
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm_logo.png" width="500em" />
+</div>
+
+<p align="center">
+<a href="https://arxiv.org/pdf/2506.07900" target="_blank">MiniCPM 技术报告</a> |
+<a href="https://github.com/OpenBMB/MiniCPM" target="_blank">GitHub 仓库</a> |
+<a href="https://ultradata.openbmb.cn/" target="_blank">UltraData</a> |
+<a href="https://github.com/OpenBMB/MiniCPM-Desk-Pet" target="_blank">MiniCPM 桌宠</a> |
+<a href="https://huggingface.co/spaces/openbmb/MiniCPM5-1B-Demo" target="_blank">在线 Demo</a>
+</p>
+
+<p align="center">
+<a href="https://huggingface.co/openbmb/MiniCPM5-1B/blob/main/README.md" target="_blank">English</a> |
+中文
+</p>
+
+## 亮点
+
+我们正式发布 **MiniCPM5-1B**，这是 **MiniCPM5** 系列的首个模型。它是一款面向端侧、本地部署和资源受限场景的 1B 稠密 Transformer，能够达到同尺寸开源模型 SOTA 水平。
+
+🏆 **同尺寸开源模型 SOTA**：与同尺寸优秀开源模型相比，MiniCPM5-1B 在该对比范围内达到 SOTA 水平，优势主要体现在 Agentic 工具调用、代码生成和高难推理。
+
+![MiniCPM5-1B 各领域能力对比](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/public_leaderboard_radar_cn.png)
+
+🧠 **双模式推理**：内置 `<think>` chat template，可通过 `enable_thinking` 在思考模式和非思考模式之间切换。同一份权重既可以作为快速助手，也可以承担更复杂的推理任务。
+
+🛠️ **部署 / 微调资源**：MiniCPM GitHub 仓库提供面向主要推理后端和微调框架的单页 cookbook，并配套 Agent Skills，方便复现部署和微调流程。
+
+🐱 **桌宠**：我们也提供了由 MiniCPM5-1B 本地驱动的桌宠应用。
+
+## 模型列表
+
+你可以按运行环境选择对应模型格式：
+
+- **[MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B) · BF16 正式版（经 RL + OPD 后训练） **👈 当前页面**
+- **[MiniCPM5-1B-SFT](https://huggingface.co/openbmb/MiniCPM5-1B-SFT)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-SFT) · BF16 SFT 单独 checkpoint（RL / OPD 之前）
+- **[MiniCPM5-1B-Base](https://huggingface.co/openbmb/MiniCPM5-1B-Base)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-Base) · BF16 base checkpoint（仅预训练）
+- **[MiniCPM5-1B-GGUF](https://huggingface.co/openbmb/MiniCPM5-1B-GGUF)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-GGUF) · GGUF，适用于 llama.cpp / Ollama / LM Studio
+- **[MiniCPM5-1B-MLX](https://huggingface.co/openbmb/MiniCPM5-1B-MLX)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-MLX) · MLX / 4bit，适用于 Apple Silicon
+
+## 模型信息
+
+MiniCPM5-1B 具有以下特性：
+
+- **类型**：Causal Language Model
+- **架构**：标准 `LlamaForCausalLM`
+- **参数数量**：1,080,632,832
+- **非嵌入参数数量**：679,552,512
+- **层数**：24
+- **注意力头（GQA）**：16 个 Q heads / 2 个 KV heads
+- **上下文长度**：131,072
+
+## 简介
+
+MiniCPM5-1B 是 MiniCPM5 系列的首个模型，面向本地助手、coding agent、工具调用流程以及需要紧凑模型的推理场景。它在较小部署成本下提供原生长上下文能力，并通过同一份权重支持 Think / No Think 两种对话模式。
+
+## 评测结果
+
+我们选取 **LFM2.5-1.2B-Thinking**、**Qwen3-0.6B/think**、**Qwen3.5-0.8B/think** 等同尺寸优秀开源模型进行横向比较。这些模型本身已经很强；在这组对比中，MiniCPM5-1B 达到同尺寸开源模型 SOTA 水平，优势主要体现在工具调用、代码生成和高难推理上，也更适合承担本地 coding agent、工具助手和推理助手的角色。
+
+![MiniCPM-5 1B 基准评测成绩](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/public_leaderboard_cn.png)
+
+## 训练流程
+
+MiniCPM5-1B 的训练过程是 **[UltraData 分级数据管理体系](https://arxiv.org/pdf/2602.09003)** 的一次完整实践，覆盖 base training、mid-training 与后训练三个阶段。
+
+**Base training** 采用逐级推进的训练配方，包含 stable training 与 decay training，用于建立基础语言能力与训练稳定性。随后进入 **mid-training**，进一步强化目标能力并适配数据分布。训练语料来自我们同步开源的 [Ultra-FineWeb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb)、[Ultra-FineWeb-L3](https://huggingface.co/datasets/openbmb/Ultra-FineWeb-L3) 与 [UltraData-Math](https://huggingface.co/datasets/openbmb/UltraData-Math)。
+
+**后训练阶段**分为 **SFT**、**RL** 与 **OPD** 三步。我们先使用 **200B tokens deep-thinking SFT** 与 **200B tokens hybrid-thinking SFT** 建立深度思考、混合思考和通用对话能力，相关 SFT 数据已同步开源为 [UltraData-SFT-2605](https://huggingface.co/datasets/openbmb/UltraData-SFT-2605)。随后针对数学、代码、闭卷问答和写作等方向训练专用 **RL teacher**，并通过 **On-Policy Distillation (OPD)** 将这些 teacher 的能力蒸馏回同一个发布模型。
+
+![MiniCPM5-1B 训练流程](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/training_recipe.png)
+
+### RL + OPD 带来了什么？
+
+**RL + OPD** 是 MiniCPM5-1B 后训练中的关键环节。在数学、代码、指令跟随三类任务上，RL + OPD 将平均分提升 **↑16 分**，同时将回复触顶 max-tokens 预算的比例降低 **↓29 个百分点**。下方图示展示 Reasoning RL 两阶段流程、分数提升和超长率下降。
+
+**RL** 阶段组合了推理、闭卷问答、写作、指令跟随、长上下文理解和通用对话等多类互补训练信号。Reasoning RL 基于 [DAPO-Math-17k](https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k) (借鉴 [JustRL](https://arxiv.org/pdf/2512.16649) 极简配方思想），并采用两阶段长度调度，以减少过长回复的同时提升推理准确性。我们还使用 [TriviaQA](https://huggingface.co/datasets/mandarjoshi/trivia_qa)、[NQ-Open](https://huggingface.co/datasets/google-research-datasets/nq_open)、[LongWriter-Zero-RLData](https://huggingface.co/datasets/THU-KEG/LongWriter-Zero-RLData)、合成可验证 RLVR 数据与 pair-wise RLHF 信号，提升可靠性、指令跟随和用户体验。
+
+![MiniCPM5-1B RL 两阶段流程](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/rl_two_stage_overview.png)
+
+**OPD** 阶段参考 Thinking Machines Lab 的 [On-Policy Distillation](https://thinkingmachines.ai/blog/on-policy-distillation/) 思路，并结合 [Rethinking On-Policy Distillation](https://arxiv.org/pdf/2604.13016) 做了实现改进。我们在强化学习框架中使用反向 KL 散度作为优势估计值，替代原有的 verification-based advantage；同时在 response 序列的每个位置分别对学生模型和教师模型 logits 做双边 top-k 采样，取并集后计算反向 KL 散度，以平衡监督信号准确性和训练效率。OPD 直接复用各 RL teacher 训练时的同分布 prompt 作为蒸馏数据，无需额外构造语料。
+
+![MiniCPM5-1B RL + OPD 增益](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/rl_gains.png)
+
+![MiniCPM5-1B RL + OPD 超长率下降](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/rl_overlong.png)
+
+## 快速上手
+
+### vLLM
+
+```bash
+pip install "vllm>=0.21"
+vllm serve openbmb/MiniCPM5-1B --port 8000
+```
+
+```bash
+curl http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "openbmb/MiniCPM5-1B",
+    "messages": [{"role": "user", "content": "你是谁？可以简单介绍一下自己吗？"}],
+    "max_tokens": 128,
+    "temperature": 0.7
+  }'
+```
+
+### SGLang
+
+```bash
+pip install "sglang[srt]>=0.5.12"
+python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000
+```
+
+```bash
+curl http://localhost:30000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "openbmb/MiniCPM5-1B",
+    "messages": [{"role": "user", "content": "你是谁？可以简单介绍一下自己吗？"}],
+    "max_tokens": 128,
+    "temperature": 0.7
+  }'
+```
+
+### Transformers
+
+```bash
+pip install -U "transformers>=5.6" accelerate torch
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "openbmb/MiniCPM5-1B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [{"role": "user", "content": "你是谁？可以简单介绍一下自己吗？"}]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    enable_thinking=False,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+推荐的 chat template 采样参数：
+
+| 模式 | 推荐采样参数 | 启用方式 |
+| --- | --- | --- |
+| **Think** | `temperature=0.9, top_p=0.95` | `enable_thinking=True` |
+| **No Think** | `temperature=0.7, top_p=0.95` | `enable_thinking=False` |
+
+## 工具调用
+
+工具调用**推荐使用 SGLang**。MiniCPM5-1B 以 XML 格式产出工具调用，SGLang 内置的 `minicpm5` parser 会自动将其转换为 OpenAI 兼容的 `tool_calls` 字段。
+
+```bash
+python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000 \
+    --tool-call-parser minicpm5      # 或：--tool-call-parser auto
+```
+
+## GitHub Cookbooks 与 Agent Skills
+
+MiniCPM5-1B 使用**标准 `LlamaForCausalLM` 架构**，主流推理引擎可直接加载，**无需自定义算子，也无模型代码 fork**。逐步部署和微调说明请参考下方 GitHub cookbooks；Agent Skills 作为 GitHub 资源提供给使用 Cursor / Claude Code 类 coding agent 的用户。
+
+### 部署
+
+| 后端 | 模型格式 / 适用场景 | Cookbook | Agent Skill |
+| --- | --- | --- | --- |
+| Transformers | BF16 / FP16，本地 Python 推理，GPU + CPU | [transformers.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/transformers.md) | [minicpm5-deploy-transformers](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-transformers/SKILL.md) |
+| vLLM | BF16 / FP16 OpenAI server | [vllm.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/vllm.md) | [minicpm5-deploy-vllm](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-vllm/SKILL.md) |
+| SGLang | BF16 / FP16 OpenAI server，推荐用于 tool calling | [sglang.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/sglang.md) | [minicpm5-deploy-sglang](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-sglang/SKILL.md) |
+| llama.cpp | GGUF，CPU/GPU 本地推理 | [llama_cpp.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/llama_cpp.md) | [minicpm5-deploy-llama-cpp](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-llama-cpp/SKILL.md) |
+| Ollama | GGUF，本地端侧运行 | [ollama.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/ollama.md) | [minicpm5-deploy-ollama](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-ollama/SKILL.md) |
+| LM Studio | GGUF，Mac 桌面应用与 OpenAI server | [lmstudio.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/lmstudio.md) | [minicpm5-deploy-lmstudio](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-lmstudio/SKILL.md) |
+| MLX | MLX / 4bit，Apple Silicon 本地推理 | [mlx.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/mlx.md) | [minicpm5-deploy-mlx](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-mlx/SKILL.md) |
+| ArcLight | GGUF 本地端侧 / CPU / 桌面 / 服务器 | [arclight.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/arclight.md) | [minicpm5-deploy-arclight](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-arclight/SKILL.md) |
+
+### 微调
+
+| 框架 | 适用场景 | Cookbook | Agent Skill |
+| --- | --- | --- | --- |
+| TRL + PEFT | LoRA / SFT 微调 | [trl.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/trl.md) | [minicpm5-finetune-trl](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-trl/SKILL.md) |
+| LLaMA-Factory | 微调 | [llamafactory.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/llamafactory.md) | [minicpm5-finetune-llamafactory](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-llamafactory/SKILL.md) |
+| ms-swift | 微调 | [ms_swift.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/ms_swift.md) | [minicpm5-finetune-ms-swift](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-ms-swift/SKILL.md) |
+| unsloth | 微调 | [unsloth.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/unsloth.md) | [minicpm5-finetune-unsloth](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-unsloth/SKILL.md) |
+| xtuner | 微调 | [xtuner.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/xtuner.md) | [minicpm5-finetune-xtuner](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-xtuner/SKILL.md) |
+
+### 其他支持的框架
+
+除上文列出的部署与微调框架外，MiniCPM5-1B 也支持通过 FlagOS 进行多芯片部署。
+
+#### FlagOS 介绍
+
+为解决不同 AI 芯片大规模落地应用，北京智源研究院联合众多科研机构、芯片企业、系统厂商、算法和软件相关单位等国内外机构共同发起并创立了 FlagOS 开源社区。
+
+FlagOS 社区致力于打造面向多种 AI 芯片的统一、开源的系统软件栈，包括大型算子库、统一AI编译器、并行训推框架、统一通信库等核心开源项目，构建「模型-系统-芯片」三层贯通的开放技术生态，通过“一次开发跨芯迁移”释放硬件计算潜力，打破不同芯片软件栈之间生态隔离，有效降低开发者的迁移成本。FlagOS 社区构建人工智能软硬件生态，突破单一闭源垄断，推动AI硬件技术大范围落地发展，立足中国、拥抱全球合作。
+
+官网速递：[https://flagos.io](https://flagos.io/)
+
+<details>
+<summary>FlagOS 多 AI 芯片支持与使用方式</summary>
+
+#### FlagOS 多 AI 芯片支持
+
+基于 FlagOS 极短时间内适配 MiniCPM5-1B 到 9 种不同的 AI 芯片，得益于众智 FlagOS 的多芯片统一 AI 系统软件栈的能力。目前，在 FlagOS 团队构建的面向多架构人工智能芯片的大模型自动迁移、适配与发布平台 FlagRelease 上，已发布 MiniCPM5-1B 的多芯片版本。细节如下：
+
+|Vendor|ModelScope|Huggingface|
+|---|---|---|
+|Nvidia|[MiniCPM5-1B-nvidia-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|[MiniCPM5-1B-nvidia-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|
+|Hygon|[MiniCPM5-1B-hygon-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|[MiniCPM5-1B-hygon-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|
+|Metax|[MiniCPM5-1B-metax-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-metax-FlagOS)|[MiniCPM5-1B-metax-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-metax-FlagOS)|
+|Iluvatar|[MiniCPM5-1B-iluvatar-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|[MiniCPM5-1B-iluvatar-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|
+|Zhenwu|[MiniCPM5-1B-zhenwu-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|[MiniCPM5-1B-zhenwu-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|
+|Mthreads|[MiniCPM5-1B-mthreads-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|[MiniCPM5-1B-mthreads-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|
+|Kunlunxin|[MiniCPM5-1B-kunlunxin-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|[MiniCPM5-1B-kunlunxin-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|
+|Ascend|[MiniCPM5-1B-ascend-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|[MiniCPM5-1B-ascend-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|
+|ARM-v9|[MiniCPM5-1B-Armv9-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|[MiniCPM5-1B-Armv9-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|
+
+#### FlagOS 使用方式
+
+##### 使用 FlagOS 在 Nvidia 体验性能加速
+
+###### From FlagRelease（**推荐**）
+
+FlagRelease是FlagOS团队构建的一套面向多架构人工智能芯片的大模型自动迁移、适配与发布平台，已发布MiniCPM-1B的多芯片版本。FlagRelase已内置相关软件包，无需用户安装。
+
+###### FlagRelease 镜像关键版本信息
+
+###### FlagRelease 使用速递
+
+|Vendor|ModelScope|Huggingface|
+|---|---|---|
+|Nvidia|[MiniCPM5-1B-nvidia-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|[MiniCPM5-1B-nvidia-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|
+|Hygon|[MiniCPM5-1B-hygon-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|[MiniCPM5-1B-hygon-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|
+|Metax|[MiniCPM5-1B-metax-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-metax-FlagOS)|[MiniCPM5-1B-metax-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-metax-FlagOS)|
+|Iluvatar|[MiniCPM5-1B-iluvatar-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|[MiniCPM5-1B-iluvatar-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|
+|Zhenwu|[MiniCPM5-1B-zhenwu-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|[MiniCPM5-1B-zhenwu-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|
+|Mthreads|[MiniCPM5-1B-mthreads-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|[MiniCPM5-1B-mthreads-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|
+|Kunlunxin|[MiniCPM5-1B-kunlunxin-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|[MiniCPM5-1B-kunlunxin-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|
+|Ascend|[MiniCPM5-1B-ascend-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|[MiniCPM5-1B-ascend-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|
+|ARM-v9|[MiniCPM5-1B-Armv9-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|[MiniCPM5-1B-Armv9-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|
+
+###### 从零开始
+
+- 依赖Python3.12, GLIBC_2.39, GLIBCXX_3.4.33, CXXABI_1.3.15 环境
+
+###### Vllm 版本 
+
+###### 安装 FlagOS 算子库
+
+官方仓库：https://github.com/flagos-ai/FlagGems
+
+```PowerShell
+pip install flag-gems==4.2.1rc0
+pip install triton==3.5.1
+```
+
+###### 开启加速
+
+通过在vllm执行推理的源码中增加flagGems的导入即可开启flagGems加速
+
+```Bash
+import flag_gems
+flag_gems.enable(record=True, once=True, path="/root/gems.txt")
+```
+
+```Bash
+vllm serve ${model_path} \
+--trust-remote-code \
+--dtype bfloat16 \
+--enforce-eager \
+--port ${Port} \
+--served-model-name ${model_name} \
+--gpu-memory-utilization 0.85
+```
+
+##### 使用 FlagOS 统一多芯片后端插件
+
+**[vllm-plugin-FL](https://github.com/flagos-ai/vllm-plugin-FL)** 是一个为 **vLLM** 推理/服务框架构建的插件，它基于 **FlagOS 的统一多芯片后端**开发，旨在扩展 vLLM 在多种硬件环境下的功能和性能表现。
+
+###### vllm-plugin-FL 使用
+
+|厂商|从零开始|从 FlagRelease 开始||
+|---|---|---|---|
+|英伟达|[vllm-plugin-FL/MiniCPM5-1B](https://github.com/flagos-ai/vllm-plugin-FL/blob/main/examples/minicpm/README.md)|[MiniCPM5-1B-ModelScope](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|[MiniCPM5-1B-nvidia-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|
+
+</details>
+
+## 桌宠
+
+我们也发布了 **[OpenBMB/MiniCPM-Desk-Pet](https://github.com/OpenBMB/MiniCPM-Desk-Pet)**，一个由 MiniCPM5-1B 本地驱动的桌宠应用。它支持 Apple Silicon / NVIDIA GPU / CPU 路线，可以与 Cursor、Claude Code、Codex 等 coding agent 联动，并支持 LoRA 人格切换。
+
+<a href="https://youtu.be/Ee0slMW8SEk"><img src="https://img.youtube.com/vi/Ee0slMW8SEk/0.jpg" alt="MiniCPM Desk Pet video demo" width="720"></a>
+
+## 局限性与负责任使用
+
+MiniCPM5-1B 是一个基于训练数据统计规律生成文本的语言模型，可能生成不准确、有偏见或不安全的内容。在高风险场景中使用前，应对模型输出进行审查和验证。
+
+用户需要自行评估模型输出，配置必要的安全防护，并遵守适用法律法规和平台政策。
+
+## 开源协议
+
+MiniCPM 模型权重与相关代码依照 [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) 协议发布。
+
+## 引用
+
+如果觉得我们的工作有帮助，请引用：
+
+```bibtex
+@article{minicpm4,
+  title={Minicpm4: Ultra-efficient llms on end devices},
+  author={MiniCPM, Team},
+  journal={arXiv preprint arXiv:2506.07900},
+  year={2025}
+}
+```
diff --git a/MiniCPM5-1B/README.md b/MiniCPM5-1B/README.md
new file mode 100644
index 0000000..a67001e
--- /dev/null
+++ b/MiniCPM5-1B/README.md
@@ -0,0 +1,351 @@
+---
+license: apache-2.0
+language:
+  - en
+  - zh
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - minicpm
+  - minicpm5
+  - llama
+  - text-generation
+  - long-context
+  - tool-calling
+  - on-device
+  - edge-ai
+datasets:
+  - openbmb/Ultra-FineWeb
+  - openbmb/Ultra-FineWeb-L3
+  - openbmb/UltraData-Math
+  - openbmb/UltraData-SFT-2605
+---
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm_logo.png" width="500em" />
+</div>
+
+<p align="center">
+<a href="https://arxiv.org/pdf/2506.07900" target="_blank">MiniCPM Tech Report</a> |
+<a href="https://github.com/OpenBMB/MiniCPM" target="_blank">GitHub Repo</a> |
+<a href="https://ultradata.openbmb.cn/" target="_blank">UltraData</a> |
+<a href="https://github.com/OpenBMB/MiniCPM-Desk-Pet" target="_blank">MiniCPM Desk Pet</a> |
+<a href="https://huggingface.co/spaces/openbmb/MiniCPM5-1B-Demo" target="_blank">Online Demo</a>
+</p>
+
+<p align="center">
+English |
+<a href="https://huggingface.co/openbmb/MiniCPM5-1B/blob/main/README-cn.md" target="_blank">中文</a>
+</p>
+
+## Highlights
+
+We are releasing **MiniCPM5-1B**, the first model in the **MiniCPM5** series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA.
+
+🏆 **1B-class open-source SOTA**: compared with strong open-source models in the same size class, MiniCPM5-1B reaches SOTA within this comparison set. Its advantage is most visible in agentic tool use, code generation, and difficult reasoning.
+
+![MiniCPM5-1B capability comparison by domain](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/public_leaderboard_radar_en.png)
+
+🧠 **Hybrid Reasoning**: built-in `<think>` chat template, switch via `enable_thinking`. The same checkpoint serves as both a fast assistant and a deliberate reasoner.
+
+🛠️ **Deployment / Fine-tuning Resources**: the MiniCPM GitHub repo provides single-page cookbooks and Agent Skills for major inference backends and fine-tuning frameworks.
+
+🐱 **Desktop Pet**: a local-LLM desktop pet driven by MiniCPM5-1B.
+
+## Model List
+
+Use this directory to choose the model format that matches your runtime:
+
+- **[MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B) · BF16 final release (post-trained with RL + OPD) **👈 you are here**
+- **[MiniCPM5-1B-SFT](https://huggingface.co/openbmb/MiniCPM5-1B-SFT)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-SFT) · BF16 SFT-only checkpoint (before RL / OPD)
+- **[MiniCPM5-1B-Base](https://huggingface.co/openbmb/MiniCPM5-1B-Base)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-Base) · BF16 base checkpoint (pre-training only)
+- **[MiniCPM5-1B-GGUF](https://huggingface.co/openbmb/MiniCPM5-1B-GGUF)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-GGUF) · GGUF for llama.cpp / Ollama / LM Studio
+- **[MiniCPM5-1B-MLX](https://huggingface.co/openbmb/MiniCPM5-1B-MLX)** · [ModelScope](https://www.modelscope.cn/models/OpenBMB/MiniCPM5-1B-MLX) · MLX / 4bit for Apple Silicon
+
+## Model Information
+
+MiniCPM5-1B has the following features:
+
+- **Type**: Causal Language Model
+- **Architecture**: Standard `LlamaForCausalLM`
+- **Number of Parameters**: 1,080,632,832
+- **Number of Non-Embedding Parameters**: 679,552,512
+- **Number of Layers**: 24
+- **Number of Attention Heads (GQA)**: 16 for Q and 2 for KV
+- **Context Length**: 131,072
+
+## Introduction
+
+MiniCPM5-1B is the first checkpoint in the MiniCPM5 series. It is designed for local assistants, coding agents, tool-use workflows, and reasoning scenarios where a compact model is preferred. The model keeps a small deployment footprint while providing native long-context support and both Think / No Think chat modes through the same checkpoint.
+
+## Evaluation Results
+
+We compare MiniCPM5-1B with strong open-source models in the same size class, including **LFM2.5-1.2B-Thinking**, **Qwen3-0.6B/think** and **Qwen3.5-0.8B/think**. These are capable baselines; within this comparison set, MiniCPM5-1B reaches 1B-class open-source SOTA, with its advantage most visible in tool use, code generation, and difficult reasoning. This makes it a practical choice for local coding agents, tool assistants, and reasoning assistants.
+
+![MiniCPM-5 1B Public Leaderboard](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/public_leaderboard_en.png)
+
+## Training Recipe
+
+The training of MiniCPM5-1B is a full-stack practice of **[UltraData Tiered Data Management](https://arxiv.org/pdf/2602.09003)**, covering three stages: base training, mid-training, and post-training.
+
+During **base training**, the model goes through stable training and decay training to build core language capability and training stability. It then enters **mid-training** to further strengthen target capabilities and adapt to the target data distribution. The training corpus is released alongside the model as [Ultra-FineWeb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb), [Ultra-FineWeb-L3](https://huggingface.co/datasets/openbmb/Ultra-FineWeb-L3), and [UltraData-Math](https://huggingface.co/datasets/openbmb/UltraData-Math).
+
+During **post-training**, we proceed in three steps: **SFT**, **RL**, and **OPD**. We first use **200B tokens of deep-thinking SFT** and **200B tokens of hybrid-thinking SFT** to establish deep-thinking, hybrid-thinking, and general chat abilities; the SFT data is released as [UltraData-SFT-2605](https://huggingface.co/datasets/openbmb/UltraData-SFT-2605). We then train specialized **RL teachers** for math, code, closed-book QA, writing, and related domains, and use **On-Policy Distillation (OPD)** to distill these teachers back into one release model.
+
+![MiniCPM5-1B Training Recipe](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/training_recipe.png)
+
+### What does RL + OPD bring?
+
+**RL + OPD** is a key part of MiniCPM5-1B post-training. On math, code and instruction-following tasks, RL + OPD raises the average score by **↑16 points** while cutting the share of responses that hit the max-tokens budget by **↓29 percentage points**. The figures below show the two-stage Reasoning RL pipeline, score gains, and the drop in overlong responses.
+
+**RL** combines complementary training signals for reasoning, closed-book QA, writing, instruction following, long-context understanding, and general dialogue. Reasoning RL is based on [DAPO-Math-17k](https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k) (inspired by [JustRL](https://arxiv.org/pdf/2512.16649)'s minimalist recipe) and uses a two-stage length schedule to reduce overlong responses while improving reasoning accuracy. We also use [TriviaQA](https://huggingface.co/datasets/mandarjoshi/trivia_qa), [NQ-Open](https://huggingface.co/datasets/google-research-datasets/nq_open), [LongWriter-Zero-RLData](https://huggingface.co/datasets/THU-KEG/LongWriter-Zero-RLData), synthesized verifiable RLVR data, and pair-wise RLHF signals to improve reliability, instruction following, and user experience.
+
+![MiniCPM5-1B RL Two-stage Pipeline](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/rl_two_stage_overview.png)
+
+**OPD** builds on Thinking Machines Lab's [On-Policy Distillation](https://thinkingmachines.ai/blog/on-policy-distillation/) and incorporates implementation improvements from [Rethinking On-Policy Distillation](https://arxiv.org/pdf/2604.13016). In the RL framework, we use reverse KL divergence as the advantage estimate, replacing the original verification-based advantage. At each response position, we take top-k logits from both the student and teacher models, compute reverse KL on the union of the two token sets, and balance the accuracy of the RKL signal with training efficiency. OPD reuses the in-domain prompts used to train each RL teacher as distillation data, so no additional data curation is required.
+
+![MiniCPM5-1B RL + OPD Gains](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/rl_gains.png)
+
+![MiniCPM5-1B RL + OPD Overlong Response Rate Drop](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/rl_overlong.png)
+
+## Quickstart
+
+### vLLM
+
+```bash
+pip install "vllm>=0.21"
+vllm serve openbmb/MiniCPM5-1B --port 8000
+```
+
+```bash
+curl http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "openbmb/MiniCPM5-1B",
+    "messages": [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}],
+    "max_tokens": 128,
+    "temperature": 0.7
+  }'
+```
+
+### SGLang
+
+```bash
+pip install "sglang[srt]>=0.5.12"
+python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000
+```
+
+```bash
+curl http://localhost:30000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "openbmb/MiniCPM5-1B",
+    "messages": [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}],
+    "max_tokens": 128,
+    "temperature": 0.7
+  }'
+```
+
+### Transformers
+
+```bash
+pip install -U "transformers>=5.6" accelerate torch
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "openbmb/MiniCPM5-1B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    enable_thinking=False,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+Recommended chat template sampling:
+
+| Mode | Recommended params | Enable |
+| --- | --- | --- |
+| **Think** | `temperature=0.9, top_p=0.95` | `enable_thinking=True` |
+| **No Think** | `temperature=0.7, top_p=0.95` | `enable_thinking=False` |
+
+## Tool Calling
+
+For tool / function calling, **SGLang is the recommended backend**. MiniCPM5-1B emits XML-style tool calls and SGLang's built-in `minicpm5` parser converts them to OpenAI-compatible `tool_calls` natively:
+
+```bash
+python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000 \
+    --tool-call-parser minicpm5      # or: --tool-call-parser auto
+```
+
+## GitHub Cookbooks and Agent Skills
+
+MiniCPM5-1B uses the **standard `LlamaForCausalLM` architecture**, so mainstream inference engines can load it directly: **no custom kernels, no model-code fork**. For step-by-step deployment and fine-tuning instructions, use the GitHub cookbooks below. Agent Skills are linked as GitHub resources for users working with Cursor / Claude Code style coding agents.
+
+### Deployment
+
+| Backend | Model format / use case | Cookbook | Agent Skill |
+| --- | --- | --- | --- |
+| Transformers | BF16 / FP16 local Python inference, GPU + CPU | [transformers.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/transformers.md) | [minicpm5-deploy-transformers](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-transformers/SKILL.md) |
+| vLLM | BF16 / FP16 OpenAI server | [vllm.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/vllm.md) | [minicpm5-deploy-vllm](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-vllm/SKILL.md) |
+| SGLang | BF16 / FP16 OpenAI server, recommended for tool calling | [sglang.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/sglang.md) | [minicpm5-deploy-sglang](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-sglang/SKILL.md) |
+| llama.cpp | GGUF local inference, CPU/GPU | [llama_cpp.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/llama_cpp.md) | [minicpm5-deploy-llama-cpp](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-llama-cpp/SKILL.md) |
+| Ollama | GGUF local on-device runtime | [ollama.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/ollama.md) | [minicpm5-deploy-ollama](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-ollama/SKILL.md) |
+| LM Studio | GGUF Mac desktop app and OpenAI server | [lmstudio.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/lmstudio.md) | [minicpm5-deploy-lmstudio](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-lmstudio/SKILL.md) |
+| MLX | MLX / 4bit local inference on Apple Silicon | [mlx.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/mlx.md) | [minicpm5-deploy-mlx](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-mlx/SKILL.md) |
+| ArcLight | GGUF local on-device, CPU, Desktop & Server | [arclight.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/arclight.md) | [minicpm5-deploy-arclight](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-arclight/SKILL.md) |
+
+### Fine-tuning
+
+| Framework | Use case | Cookbook | Agent Skill |
+| --- | --- | --- | --- |
+| TRL + PEFT | LoRA / SFT fine-tuning | [trl.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/trl.md) | [minicpm5-finetune-trl](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-trl/SKILL.md) |
+| LLaMA-Factory | Fine-tuning | [llamafactory.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/llamafactory.md) | [minicpm5-finetune-llamafactory](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-llamafactory/SKILL.md) |
+| ms-swift | Fine-tuning | [ms_swift.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/ms_swift.md) | [minicpm5-finetune-ms-swift](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-ms-swift/SKILL.md) |
+| unsloth | Fine-tuning | [unsloth.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/unsloth.md) | [minicpm5-finetune-unsloth](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-unsloth/SKILL.md) |
+| xtuner | Fine-tuning | [xtuner.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/finetune/xtuner.md) | [minicpm5-finetune-xtuner](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-finetune-xtuner/SKILL.md) |
+
+### Other Supported Frameworks
+
+In addition to the deployment and fine-tuning frameworks listed above, MiniCPM5-1B is also supported by FlagOS for multi-chip deployment.
+
+#### FlagOS Overview
+
+To enable large-scale deployment across different AI chips, Beijing Zhiyuan Research Institute, together with numerous research institutions, chip manufacturers, system vendors, and algorithm and software organizations both domestically and internationally, jointly initiated and established the FlagOS Open Source Community.
+
+The FlagOS community is dedicated to building a unified, open-source system software stack for various AI chips, encompassing core open-source projects such as a large-scale operator library, a unified AI compiler, parallel training and inference frameworks, and a unified communication library. It aims to create an open technology ecosystem connecting the “model-system-chip” layers. By enabling “develop once, deploy across chips”, FlagOS unlocks the computational potential of hardware, breaks down the ecosystem silos between different chip software stacks, and effectively reduces migration costs for developers.The FlagOS community fosters an AI hardware and software ecosystem, overcomes single-vendor closed-source monopolies, promotes widespread deployment of AI hardware technologies, and is committed to rooted in China while embracing global collaboration.
+
+Official website express: [https://flagos.io](https://flagos.io/)
+
+<details>
+<summary>FlagOS multi-chip support and usage</summary>
+
+#### FlagOS: Supporting Multiple AI Chips
+
+Thanks to FlagOS’s unified multi-chip AI system software stack, MiniCPM5-1B was adapted to 4–5 different AI chips in an extremely short time. Currently, the multi-chip version of MiniCPM5-1B has been released on FlagRelease, FlagOS’s platform for automatic migration, adaptation, and deployment of large models across multi-architecture AI chips. Details are as follows:
+
+|Vendor|ModelScope|Huggingface|
+|---|---|---|
+|Nvidia|[MiniCPM5-1B-nvidia-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|[MiniCPM5-1B-nvidia-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|
+|Hygon|[MiniCPM5-1B-hygon-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|[MiniCPM5-1B-hygon-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|
+|Metax|[MiniCPM5-1B-metax-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-metax-FlagOS)|[MiniCPM5-1B-metax-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-metax-FlagOS)|
+|Iluvatar|[MiniCPM5-1B-iluvatar-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|[MiniCPM5-1B-iluvatar-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|
+|Zhenwu|[MiniCPM5-1B-zhenwu-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|[MiniCPM5-1B-zhenwu-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|
+|Mthreads|[MiniCPM5-1B-mthreads-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|[MiniCPM5-1B-mthreads-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|
+|Kunlunxin|[MiniCPM5-1B-kunlunxin-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|[MiniCPM5-1B-kunlunxin-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|
+|Ascend|[MiniCPM5-1B-ascend-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|[MiniCPM5-1B-ascend-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|
+|ARM-v9|[MiniCPM5-1B-Armv9-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|[MiniCPM5-1B-Armv9-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|
+
+#### FlagOS Usage
+
+##### FlagOS Performance Acceleration on Nvidia
+
+###### From FlagRelease (**Recommendation**)
+
+FlagRelease is a platform developed by the FlagOS team for automatic migration, adaptation, and deployment of large models across multi-architecture AI chips. The multi-chip version of MiniCPM5-1B has already been released on FlagRelease. All necessary software packages are pre-installed on the platform, so users do not need to install anything.
+
+###### FlagRelease Image Key Versions
+
+###### FlagRelease Quick Start
+
+|Vendor|ModelScope|Huggingface|
+|---|---|---|
+|Nvidia|[MiniCPM5-1B-nvidia-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|[MiniCPM5-1B-nvidia-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|
+|Hygon|[MiniCPM5-1B-hygon-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|[MiniCPM5-1B-hygon-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-hygon-FlagOS)|
+|Metax|[MiniCPM5-1B-metax-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-metax-FlagOS)|[MiniCPM5-1B-metax-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-metax-FlagOS)|
+|Iluvatar|[MiniCPM5-1B-iluvatar-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|[MiniCPM5-1B-iluvatar-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-iluvatar-FlagOS)|
+|Zhenwu|[MiniCPM5-1B-zhenwu-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|[MiniCPM5-1B-zhenwu-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-zhenwu-FlagOS)|
+|Mthreads|[MiniCPM5-1B-mthreads-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|[MiniCPM5-1B-mthreads-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-mthreads-FlagOS)|
+|Kunlunxin|[MiniCPM5-1B-kunlunxin-FlagOS](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|[MiniCPM5-1B-kunlunxin-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-kunlunxin-FlagOS)|
+|Ascend|[MiniCPM5-1B-ascend-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|[MiniCPM5-1B-ascend-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-ascend-FlagOS)|
+|ARM-v9|[MiniCPM5-1B-Armv9-FlagOS](https://modelscope.cn/models/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|[MiniCPM5-1B-Armv9-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-Armv9-FlagOS)|
+
+###### From Scratch
+
+- Dependencies: Python 3.12, GLIBC 2.39, GLIBCXX 3.4.33, CXXABI 1.3.15
+
+###### Vllm Version
+
+###### Installing the FlagOS Operator Library
+
+Official Repository: https://github.com/flagos-ai/FlagGems
+
+```PowerShell
+pip install flag-gems==4.2.1rc0
+pip install triton==3.5.1
+```
+
+###### Activating Acceleration
+
+You can enable flagGems acceleration by adding the import of flagGems in the source code of vllm where inference is performed.
+
+```Bash
+import flag_gems
+flag_gems.enable(record=True, once=True, path="/root/gems.txt")
+```
+
+```PowerShell
+vllm serve ${model_path} \
+--trust-remote-code \
+--dtype bfloat16 \
+--enforce-eager \
+--port ${Port} \
+--served-model-name ${model_name} \
+--gpu-memory-utilization 0.85
+```
+
+##### Using FlagOS Unified Multi-Chip Backend Plugin
+
+[**vllm-plugin-FL**](https://github.com/flagos-ai/vllm-plugin-FL) is a plugin built for the vLLM inference/service framework. Developed on top of FlagOS’s unified multi-chip backend, it is designed to extend vLLM’s capabilities and performance across a variety of hardware environments.
+
+###### Using vllm-plugin-FL
+
+|Vendor|From Scratch|From FlagRelease||
+|---|---|---|---|
+|Nvidia|[vllm-plugin-FL/MiniCPM5-1B](https://github.com/flagos-ai/vllm-plugin-FL/blob/main/examples/minicpm/README.md)|[MiniCPM5-1B-ModelScope](https://www.modelscope.cn/models/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|[MiniCPM5-1B-nvidia-FlagOS](https://huggingface.co/FlagRelease/MiniCPM5-1B-nvidia-FlagOS)|
+
+</details>
+
+## Desktop Pet
+
+We also ship **[OpenBMB/MiniCPM-Desk-Pet](https://github.com/OpenBMB/MiniCPM-Desk-Pet)**, a desktop pet driven locally by MiniCPM5-1B. It supports Apple Silicon / NVIDIA GPU / CPU paths, can work with coding agents such as Cursor, Claude Code, and Codex, and supports LoRA persona switching.
+
+<a href="https://youtu.be/Ee0slMW8SEk"><img src="https://img.youtube.com/vi/Ee0slMW8SEk/0.jpg" alt="MiniCPM Desk Pet video demo" width="720"></a>
+
+## Limitations and Responsible Use
+
+MiniCPM5-1B is a language model that generates content based on learned statistical patterns from training data. It may produce inaccurate, biased, or unsafe outputs, and generated content should be reviewed and verified before use in high-stakes settings.
+
+Users are responsible for evaluating outputs, applying appropriate safeguards, and complying with applicable laws, regulations, and platform policies.
+
+## License
+
+This repository and MiniCPM model weights are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
+
+## Citation
+
+Please cite our paper if you find our work valuable:
+
+```bibtex
+@article{minicpm4,
+  title={Minicpm4: Ultra-efficient llms on end devices},
+  author={MiniCPM, Team},
+  journal={arXiv preprint arXiv:2506.07900},
+  year={2025}
+}
+```
diff --git a/MiniCPM5-1B/chat_template.jinja b/MiniCPM5-1B/chat_template.jinja
new file mode 100644
index 0000000..cb2934c
--- /dev/null
+++ b/MiniCPM5-1B/chat_template.jinja
@@ -0,0 +1,179 @@
+{{- bos_token }}{%- if tools %}
+    {%- set tool_definitions %}
+        {{- "# Tools\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+        {%- for tool in tools %}
+            {{- "\n" }}
+            {{- tool | tojson(ensure_ascii=False) }}
+        {%- endfor %}
+        {{- '\n</tools>\n\nTool usage guidelines:\n- You may call zero or more functions. If no function calls are needed, just answer normally and do not include any <function ... </function>.\n- When calling a function, return an XML object within <function ... </function> using:\n<function name="function-name"><param name="param-name">param-value</param></function>\n- param-value may be multi-line. If it contains <, & or newline characters, wrap it in a CDATA block: <param name="param-name"><![CDATA[...multi-line value...]]></param>' }}
+    {%- endset %}
+    
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- if '<tool_def_sep>' in messages[0].content %}
+            {{- messages[0].content.replace('<tool_def_sep>', tool_definitions) }}
+        {%- else %}
+            {{- messages[0].content + '\n\n' + tool_definitions }}
+        {%- endif %}
+    {%- else %}
+        {{- tool_definitions.lstrip() }}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        
+        {%- if message.tool_calls %}
+            {%- set content_parts = content.split('<tool_sep>') %}
+            {%- set processed_content = content_parts[0] %}
+            {%- set tool_calls_count = message.tool_calls|length %}
+            {%- set tool_sep_count = content_parts|length - 1 %}
+            {%- set min_count = [tool_calls_count, tool_sep_count]|min %}
+            
+            {%- for i in range(1, content_parts|length) %}
+                {%- set tool_index = i - 1 %}
+                {%- if tool_index < tool_calls_count %}
+                    {%- set tool_call = message.tool_calls[tool_index] %}
+                    {%- if tool_call.function %}
+                        {%- set tool_call = tool_call.function %}
+                    {%- endif %}
+                    {%- set single_tool_xml %}
+                        {{- '<function name="' ~ tool_call.name ~ '">' }}
+                        {%- if tool_call.arguments %}
+                            {%- set args_dict = tool_call.arguments %}
+                            {%- for param_name, param_value in args_dict.items() %}
+                                {{- '<param name="' ~ param_name ~ '">' }}
+                                {%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
+                                    {{- '<![CDATA[' + param_value + ']]>' }}
+                                {%- else %}
+                                    {{- param_value }}
+                                {%- endif %}
+                                {{- '</param>' }}
+                            {%- endfor %}
+                        {%- endif %}
+                        {{- '</function>' }}
+                    {%- endset %}
+                    {%- set processed_content = processed_content + single_tool_xml + content_parts[i] %}
+                {%- else %}
+                    {%- set processed_content = processed_content + content_parts[i] %}
+                {%- endif %}
+            {%- endfor %}
+            
+            {%- if tool_calls_count > tool_sep_count %}
+                {%- for remaining_index in range(tool_sep_count, tool_calls_count) %}
+                    {%- set tool_call = message.tool_calls[remaining_index] %}
+                    {%- if tool_call.function %}
+                        {%- set tool_call = tool_call.function %}
+                    {%- endif %}
+                    {%- set remaining_tool_xml %}
+                        {{- '<function name="' ~ tool_call.name ~ '">' }}
+                        {%- if tool_call.arguments %}
+                            {%- set args_dict = tool_call.arguments %}
+                            {%- for param_name, param_value in args_dict.items() %}
+                                {{- '<param name="' ~ param_name ~ '">' }}
+                                {%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
+                                    {{- '<![CDATA[' + param_value + ']]>' }}
+                                {%- else %}
+                                    {{- param_value }}
+                                {%- endif %}
+                                {{- '</param>' }}
+                            {%- endfor %}
+                        {%- endif %}
+                        {{- '</function>' }}
+                    {%- endset %}
+                    {%- set processed_content = processed_content + remaining_tool_xml %}
+                {%- endfor %}
+            {%- endif %}
+            
+            {%- set content = processed_content %}
+        {%- endif %}
+        
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if reasoning_content %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        
+        {%- if message.tool_calls and not has_tool_sep %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<function name="' ~ tool_call.name ~ '">' }}
+                {%- if tool_call.arguments %}
+                    {%- set args_dict = tool_call.arguments %}
+                    {%- for param_name, param_value in args_dict.items() %}
+                        {{- '<param name="' ~ param_name ~ '">' }}
+                        {%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
+                            {{- '<![CDATA[' + param_value + ']]>' }}
+                        {%- else %}
+                            {{- param_value }}
+                        {%- endif %}
+                        {{- '</param>' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {%- if message.content is string %}
+            {{- content }}
+        {%- else %}
+            {{- message.content | tojson(ensure_ascii=False) }}
+        {%- endif %}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined %}
+        {%- if enable_thinking is false %}
+            {{- '<think>\n\n</think>\n\n' }}
+        {%- elif enable_thinking is true %}
+            {{- '<think>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endif %}
diff --git a/MiniCPM5-1B/config.json b/MiniCPM5-1B/config.json
new file mode 100644
index 0000000..e088beb
--- /dev/null
+++ b/MiniCPM5-1B/config.json
@@ -0,0 +1,30 @@
+{
+    "_name_or_path": "openbmb/MiniCPM5-1B",
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "bos_token_id": 0,
+    "eos_token_id": [
+        1,
+        130073
+    ],
+    "pad_token_id": 1,
+    "hidden_act": "silu",
+    "hidden_size": 1536,
+    "initializer_range": 0.02,
+    "intermediate_size": 4608,
+    "max_position_embeddings": 131072,
+    "model_type": "llama",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "head_dim": 128,
+    "rms_norm_eps": 1e-06,
+    "rope_theta": 5000000,
+    "rope_scaling": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "5.6.2",
+    "use_cache": true,
+    "vocab_size": 130560
+}
diff --git a/MiniCPM5-1B/generation_config.json b/MiniCPM5-1B/generation_config.json
new file mode 100644
index 0000000..73dc79c
--- /dev/null
+++ b/MiniCPM5-1B/generation_config.json
@@ -0,0 +1,13 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": [
+    1,
+    130073
+  ],
+  "pad_token_id": 1,
+  "do_sample": true,
+  "temperature": 0.9,
+  "top_p": 0.95,
+  "transformers_version": "5.6.2"
+}
diff --git a/MiniCPM5-1B/model-00000-of-00001.safetensors b/MiniCPM5-1B/model-00000-of-00001.safetensors
new file mode 100644
index 0000000..5a60719
--- /dev/null
+++ b/MiniCPM5-1B/model-00000-of-00001.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ab8fd86563125929be78aeec8cb3969c7ed2ead3be1ab9d3ec0a9fa69c8660d
+size 2161290912
diff --git a/MiniCPM5-1B/model.safetensors.index.json b/MiniCPM5-1B/model.safetensors.index.json
new file mode 100644
index 0000000..cf879b0
--- /dev/null
+++ b/MiniCPM5-1B/model.safetensors.index.json
@@ -0,0 +1,226 @@
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+    },
+    "130556": {
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+    },
+    "130557": {
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+    },
+    "130558": {
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+    "130559": {
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}
diff --git a/model.mpk b/model.mpk
new file mode 100644
index 0000000..ae15029
Binary files /dev/null and b/model.mpk differ
diff --git a/model/model.mpk b/model/model.mpk
new file mode 100644
index 0000000..65efa38
Binary files /dev/null and b/model/model.mpk differ
diff --git a/readme.md b/readme.md
new file mode 100644
index 0000000..3ee2524
--- /dev/null
+++ b/readme.md
@@ -0,0 +1,107 @@
+# MiniCPM5-1B Burn
+
+使用 [Burn](https://github.com/tracel-ai/burn) 框架从零实现的 MiniCPM5-1B 模型。
+
+## 功能
+
+- **模型转换**：将 safetensors 格式转换为 Burn 原生 bincode 格式
+- **模型推理**：使用 WGPU 后端运行 MiniCPM5-1B 进行文本生成，支持无上限输出
+- **独立输出**：转换后的模型文件放在 `model/` 目录，方便移植到其他项目
+
+## 架构
+
+- 24 层 Transformer
+- GQA：16 个查询头，2 个 KV 头
+- RoPE 旋转位置编码（rope_theta = 5000000）
+- RMSNorm 归一化
+- SiLU 激活函数（FFN: gate + up + down）
+- KV Cache 优化
+
+## 使用
+
+### 准备模型
+
+将 MiniCPM5-1B 模型放到 `MiniCPM5-1B/` 目录：
+```
+MiniCPM5-1B/
+├── config.json
+├── tokenizer.json
+└── model-00000-of-00001.safetensors
+```
+
+### 1. 转换模型
+
+```bash
+# 默认输出到 model/model.bin
+cargo run --release -- convert
+
+# 指定输出路径
+cargo run --release -- convert --output my_model/model.bin
+```
+
+### 2. 运行推理
+
+```bash
+# 无上限生成（直到遇到 eos token）
+cargo run --release -- run --text "你好"
+
+# 限制最大生成 token 数
+cargo run --release -- run --text "你好" --max-tokens 100
+```
+
+## 移植到其他项目
+
+转换后的文件：
+```
+model/
+└── model.bin    # Burn bincode 格式模型
+```
+
+在其他项目中加载：
+```rust
+use burn::record::{BinFileRecorder, FullPrecisionSettings};
+use minicpm_burn_lib::{LlamaConfig, LlamaForCausalLM};
+
+let config = LlamaConfig::from_json("config.json")?;
+let model = LlamaForCausalLM::<Backend>::new(config, &device);
+let model = model.load_file("model/model.bin", &BinFileRecorder::<FullPrecisionSettings>::new())?;
+```
+
+## 项目结构
+
+```
+src/
+├── lib.rs              # 库入口
+├── main.rs             # CLI 工具
+├── config.rs           # 模型配置
+├── exporter.rs         # 模型转换
+├── model/              # 模型架构
+│   ├── mod.rs
+│   ├── attention.rs    # GQA 注意力 + KV Cache
+│   ├── decoder.rs      # 解码器层
+│   ├── ffn.rs          # 前馈网络
+│   ├── model.rs        # LlamaForCausalLM
+│   ├── norm.rs         # RMSNorm
+│   └── rope.rs         # RoPE 位置编码
+└── inference/          # 推理工具
+    ├── mod.rs
+    ├── generation.rs   # 文本生成（greedy + KV Cache）
+    ├── loader.rs       # safetensors 加载器
+    └── tokenizer.rs    # 分词器封装
+```
+
+## 依赖
+
+- `burn` 0.21 - 深度学习框架（WGPU 后端）
+- `burn-store` - safetensors 支持
+- `tokenizers` - HuggingFace 分词器
+- `clap` - CLI 参数解析
+
+## 性能
+
+- WGPU Vulkan 后端：约 10 tokens/s
+- 模型大小：约 4.0 GB（F32）
+
+## 许可证
+
+MIT
\ No newline at end of file
diff --git a/src/config.rs b/src/config.rs
new file mode 100644
index 0000000..966df89
--- /dev/null
+++ b/src/config.rs
@@ -0,0 +1,62 @@
+use serde::Deserialize;
+
+#[derive(Debug, Clone, Deserialize)]
+pub struct LlamaConfig {
+    pub hidden_size: usize,
+    pub intermediate_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub num_key_value_heads: usize,
+    pub vocab_size: usize,
+    pub hidden_act: String,
+    pub rms_norm_eps: f64,
+    pub rope_theta: f64,
+    pub max_position_embeddings: usize,
+    pub bos_token_id: u32,
+    pub eos_token_id: EosTokenId,
+    pub pad_token_id: u32,
+    pub tie_word_embeddings: bool,
+    pub head_dim: Option<usize>,
+}
+
+#[derive(Debug, Clone, Deserialize)]
+#[serde(untagged)]
+pub enum EosTokenId {
+    Single(u32),
+    Multiple(Vec<u32>),
+}
+
+impl EosTokenId {
+    pub fn first(&self) -> u32 {
+        match self {
+            EosTokenId::Single(id) => *id,
+            EosTokenId::Multiple(ids) => ids[0],
+        }
+    }
+
+    pub fn contains(&self, token: u32) -> bool {
+        match self {
+            EosTokenId::Single(id) => *id == token,
+            EosTokenId::Multiple(ids) => ids.contains(&token),
+        }
+    }
+
+    pub fn as_slice(&self) -> &[u32] {
+        match self {
+            EosTokenId::Single(id) => std::slice::from_ref(id),
+            EosTokenId::Multiple(ids) => ids.as_slice(),
+        }
+    }
+}
+
+impl LlamaConfig {
+    pub fn head_dim(&self) -> usize {
+        self.head_dim.unwrap_or(self.hidden_size / self.num_attention_heads)
+    }
+
+    pub fn from_json(path: &str) -> anyhow::Result<Self> {
+        let content = std::fs::read_to_string(path)?;
+        let config: LlamaConfig = serde_json::from_str(&content)?;
+        Ok(config)
+    }
+}
diff --git a/src/exporter.rs b/src/exporter.rs
new file mode 100644
index 0000000..83b8121
--- /dev/null
+++ b/src/exporter.rs
@@ -0,0 +1,34 @@
+use crate::config::LlamaConfig;
+use crate::inference::load_safetensors;
+use crate::model::LlamaForCausalLM;
+use burn::module::Module;
+use burn::record::{FullPrecisionSettings, NamedMpkFileRecorder};
+use burn::tensor::backend::Backend;
+use std::path::Path;
+
+pub fn export_model<B: Backend>(
+    safetensors_path: &Path,
+    config_path: &Path,
+    output_dir: &Path,
+    device: &B::Device,
+) -> anyhow::Result<()> {
+    println!("开始转换 MiniCPM 模型为 Burn 格式...");
+
+    println!("加载配置文件: {:?}", config_path);
+    let config = LlamaConfig::from_json(config_path.to_str().unwrap())?;
+
+    println!("创建模型结构...");
+    let model = LlamaForCausalLM::<B>::new(config, device);
+
+    println!("加载 safetensors 权重...");
+    let model = load_safetensors(model, safetensors_path, device)?;
+
+    println!("保存为 MPK 格式...");
+    std::fs::create_dir_all(output_dir)?;
+    let output_path = output_dir.join("model");
+    let recorder = NamedMpkFileRecorder::<FullPrecisionSettings>::new();
+    model.save_file(&output_path, &recorder)?;
+
+    println!("模型已成功导出到: {:?}", output_dir);
+    Ok(())
+}
diff --git a/src/inference/generation.rs b/src/inference/generation.rs
new file mode 100644
index 0000000..5a1f1cf
--- /dev/null
+++ b/src/inference/generation.rs
@@ -0,0 +1,169 @@
+use burn::tensor::backend::Backend;
+use burn::tensor::{Float, Int, Tensor};
+use rand::Rng;
+
+use crate::config::EosTokenId;
+use crate::model::{LlamaForCausalLM, LlamaKVCache};
+
+pub struct GenerationConfig {
+    pub max_new_tokens: Option<usize>,
+    pub temperature: f32,
+    pub top_p: f32,
+}
+
+impl Default for GenerationConfig {
+    fn default() -> Self {
+        Self {
+            max_new_tokens: None,
+            temperature: 1.0,
+            top_p: 1.0,
+        }
+    }
+}
+
+pub fn generate_with_cache<B: Backend>(
+    model: &LlamaForCausalLM<B>,
+    input_ids: &[u32],
+    config: &GenerationConfig,
+    eos_token_id: &EosTokenId,
+    device: &B::Device,
+) -> Vec<u32> {
+    let mut output_ids = input_ids.to_vec();
+    let mut cache: Option<LlamaKVCache<B>> = None;
+
+    // 第一步：输入完整 prompt，建立初始 cache
+    {
+        let input_ints: Vec<i64> = input_ids.iter().map(|&x| x as i64).collect();
+        let input_tensor =
+            Tensor::<B, 1, Int>::from_ints(input_ints.as_slice(), device)
+                .unsqueeze::<2>();
+
+        let (logits, new_cache) = model.forward_with_cache(input_tensor, cache.as_ref());
+        cache = Some(new_cache);
+
+        let next_token = sample_last(&logits, config.temperature, config.top_p);
+        output_ids.push(next_token);
+
+        if eos_token_id.contains(next_token) {
+            return output_ids;
+        }
+    }
+
+    // 后续步骤：每次只输入 1 个 token，使用 cache
+    let mut count = 1;
+    loop {
+        if let Some(max) = config.max_new_tokens {
+            if count >= max {
+                break;
+            }
+        }
+
+        let last_token = output_ids[output_ids.len() - 1];
+        let input_ints: Vec<i64> = vec![last_token as i64];
+        let input_tensor =
+            Tensor::<B, 1, Int>::from_ints(input_ints.as_slice(), device)
+                .unsqueeze::<2>();
+
+        let (logits, new_cache) = model.forward_with_cache(input_tensor, cache.as_ref());
+        cache = Some(new_cache);
+
+        let vocab_size = model.config.vocab_size;
+        let next_token_logits = logits
+            .clone()
+            .slice([0..1, 0..1, 0..vocab_size])
+            .reshape([vocab_size]);
+
+        let next_token = sample(&next_token_logits, config.temperature, config.top_p);
+        output_ids.push(next_token);
+
+        if eos_token_id.contains(next_token) {
+            break;
+        }
+
+        count += 1;
+    }
+
+    output_ids
+}
+
+fn sample_last<B: Backend>(logits: &Tensor<B, 3, Float>, temperature: f32, top_p: f32) -> u32 {
+    let shape = logits.shape();
+    let dims: [usize; 3] = shape.dims();
+    let seq_len = dims[1];
+    let vocab_size = dims[2];
+
+    let last_logits = logits
+        .clone()
+        .slice([0..1, seq_len - 1..seq_len, 0..vocab_size])
+        .reshape([vocab_size]);
+
+    sample(&last_logits, temperature, top_p)
+}
+
+fn sample<B: Backend>(logits: &Tensor<B, 1, Float>, temperature: f32, top_p: f32) -> u32 {
+    let data = logits.clone().to_data();
+    let values: Vec<f32> = data.as_slice().unwrap().to_vec();
+
+    // temperature = 0 时退化为 greedy
+    if temperature <= 0.001 {
+        let mut max_idx = 0;
+        let mut max_val = f32::NEG_INFINITY;
+        for (i, &val) in values.iter().enumerate() {
+            if val > max_val {
+                max_val = val;
+                max_idx = i;
+            }
+        }
+        return max_idx as u32;
+    }
+
+    // 应用 temperature
+    let scaled: Vec<f32> = values.iter().map(|&v| v / temperature).collect();
+
+    // softmax
+    let max_val = scaled.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
+    let exp_sum: f32 = scaled.iter().map(|&v| (v - max_val).exp()).sum();
+    let mut probs: Vec<f32> = scaled.iter().map(|&v| (v - max_val).exp() / exp_sum).collect();
+
+    // top_p 过滤
+    if top_p < 1.0 {
+        let mut indexed: Vec<(usize, f32)> = probs.iter().enumerate().map(|(i, &p)| (i, p)).collect();
+        indexed.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
+
+        let mut cumsum = 0.0;
+        let mut cutoff = indexed.len();
+        for (i, &(_, p)) in indexed.iter().enumerate() {
+            cumsum += p;
+            if cumsum > top_p {
+                cutoff = i + 1;
+                break;
+            }
+        }
+
+        let keep: std::collections::HashSet<usize> = indexed[..cutoff].iter().map(|(i, _)| *i).collect();
+        for (i, p) in probs.iter_mut().enumerate() {
+            if !keep.contains(&i) {
+                *p = 0.0;
+            }
+        }
+
+        let sum: f32 = probs.iter().sum();
+        if sum > 0.0 {
+            for p in probs.iter_mut() {
+                *p /= sum;
+            }
+        }
+    }
+
+    // 多项式采样
+    let mut rng = rand::thread_rng();
+    let r: f32 = rng.gen();
+    let mut cumsum = 0.0;
+    for (i, &p) in probs.iter().enumerate() {
+        cumsum += p;
+        if r < cumsum {
+            return i as u32;
+        }
+    }
+    probs.len() as u32 - 1
+}
diff --git a/src/inference/loader.rs b/src/inference/loader.rs
new file mode 100644
index 0000000..67ab6ec
--- /dev/null
+++ b/src/inference/loader.rs
@@ -0,0 +1,226 @@
+use std::collections::HashMap;
+
+use crate::model::LlamaForCausalLM;
+use burn::module::{Module, Param};
+use burn::nn::{EmbeddingRecord, LinearRecord};
+use burn::tensor::backend::Backend;
+use burn::tensor::{Float, Shape, Tensor, TensorData};
+use memmap2::Mmap;
+use std::path::Path;
+
+pub fn load_safetensors<B: Backend>(
+    model: LlamaForCausalLM<B>,
+    path: &Path,
+    device: &B::Device,
+) -> anyhow::Result<LlamaForCausalLM<B>> {
+    let file = std::fs::File::open(path)?;
+    let mmap = unsafe { Mmap::map(&file)? };
+
+    let header_len = u64::from_le_bytes(mmap[..8].try_into().unwrap()) as usize;
+    let header_bytes = &mmap[8..8 + header_len];
+    let header: serde_json::Value = serde_json::from_slice(header_bytes)?;
+
+    let mut tensors = HashMap::new();
+    let mut shapes = HashMap::new();
+    let mut dtypes = HashMap::new();
+
+    let data_offset = 8 + header_len;
+
+    if let Some(obj) = header.as_object() {
+        for (name, info) in obj {
+            if name == "__metadata__" {
+                continue;
+            }
+            if let Some(info_obj) = info.as_object() {
+                if let Some(offsets) = info_obj.get("data_offsets") {
+                    if let Some(arr) = offsets.as_array() {
+                        let start = arr[0].as_u64().unwrap() as usize;
+                        let end = arr[1].as_u64().unwrap() as usize;
+                        let data = &mmap[data_offset + start..data_offset + end];
+                        tensors.insert(name.clone(), data.to_vec());
+                    }
+                }
+                if let Some(shape) = info_obj.get("shape") {
+                    if let Some(arr) = shape.as_array() {
+                        let shape_vec: Vec<usize> = arr
+                            .iter()
+                            .map(|v| v.as_u64().unwrap() as usize)
+                            .collect();
+                        shapes.insert(name.clone(), shape_vec);
+                    }
+                }
+                if let Some(dtype) = info_obj.get("dtype") {
+                    if let Some(s) = dtype.as_str() {
+                        dtypes.insert(name.clone(), s.to_string());
+                    }
+                }
+            }
+        }
+    }
+
+    let mut model = model;
+
+    if let Some(weight) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, "model.embed_tokens.weight", device) {
+        let record = EmbeddingRecord {
+            weight: Param::from_tensor(weight),
+        };
+        model.model.embed_tokens = model.model.embed_tokens.clone().load_record(record);
+    }
+
+    for i in 0..model.config.num_hidden_layers {
+        let prefix = format!("model.layers.{i}");
+
+        if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.self_attn.q_proj.weight"), device) {
+            let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
+            model.model.layers[i].self_attn.q_proj = model.model.layers[i].self_attn.q_proj.clone().load_record(record);
+        }
+        if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.self_attn.k_proj.weight"), device) {
+            let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
+            model.model.layers[i].self_attn.k_proj = model.model.layers[i].self_attn.k_proj.clone().load_record(record);
+        }
+        if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.self_attn.v_proj.weight"), device) {
+            let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
+            model.model.layers[i].self_attn.v_proj = model.model.layers[i].self_attn.v_proj.clone().load_record(record);
+        }
+        if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.self_attn.o_proj.weight"), device) {
+            let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
+            model.model.layers[i].self_attn.o_proj = model.model.layers[i].self_attn.o_proj.clone().load_record(record);
+        }
+
+        if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.mlp.gate_proj.weight"), device) {
+            let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
+            model.model.layers[i].mlp.gate_proj = model.model.layers[i].mlp.gate_proj.clone().load_record(record);
+        }
+        if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.mlp.up_proj.weight"), device) {
+            let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
+            model.model.layers[i].mlp.up_proj = model.model.layers[i].mlp.up_proj.clone().load_record(record);
+        }
+        if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.mlp.down_proj.weight"), device) {
+            let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
+            model.model.layers[i].mlp.down_proj = model.model.layers[i].mlp.down_proj.clone().load_record(record);
+        }
+
+        if let Some(w) = load_tensor_1d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.input_layernorm.weight"), device) {
+            model.model.layers[i].input_layernorm.weight = Param::from_tensor(w);
+        }
+        if let Some(w) = load_tensor_1d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.post_attention_layernorm.weight"), device) {
+            model.model.layers[i].post_attention_layernorm.weight = Param::from_tensor(w);
+        }
+    }
+
+    if let Some(w) = load_tensor_1d::<B>(&tensors, &shapes, &dtypes, "model.norm.weight", device) {
+        model.model.norm.weight = Param::from_tensor(w);
+    }
+
+    if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, "lm_head.weight", device) {
+        let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
+        model.lm_head = model.lm_head.clone().load_record(record);
+    } else if model.config.tie_word_embeddings {
+        let embed_weight = model.model.embed_tokens.weight.val();
+        let record = LinearRecord { weight: Param::from_tensor(embed_weight.transpose()), bias: None };
+        model.lm_head = model.lm_head.clone().load_record(record);
+    }
+
+    Ok(model)
+}
+
+fn load_tensor_2d<B: Backend>(
+    tensors: &HashMap<String, Vec<u8>>,
+    shapes: &HashMap<String, Vec<usize>>,
+    dtypes: &HashMap<String, String>,
+    name: &str,
+    device: &B::Device,
+) -> Option<Tensor<B, 2, Float>> {
+    let data = tensors.get(name)?;
+    let shape = shapes.get(name)?;
+    let dtype = dtypes.get(name)?;
+
+    assert_eq!(shape.len(), 2, "Expected 2D tensor for {name}");
+
+    let f32_data = convert_to_f32(data, dtype);
+    let shape_arr: [usize; 2] = [shape[0], shape[1]];
+    let tensor_data = TensorData::new(f32_data, Shape::new(shape_arr));
+    Some(Tensor::from_data(tensor_data, device))
+}
+
+fn load_tensor_1d<B: Backend>(
+    tensors: &HashMap<String, Vec<u8>>,
+    shapes: &HashMap<String, Vec<usize>>,
+    dtypes: &HashMap<String, String>,
+    name: &str,
+    device: &B::Device,
+) -> Option<Tensor<B, 1, Float>> {
+    let data = tensors.get(name)?;
+    let shape = shapes.get(name)?;
+    let dtype = dtypes.get(name)?;
+
+    assert_eq!(shape.len(), 1, "Expected 1D tensor for {name}");
+
+    let f32_data = convert_to_f32(data, dtype);
+    let shape_arr: [usize; 1] = [shape[0]];
+    let tensor_data = TensorData::new(f32_data, Shape::new(shape_arr));
+    Some(Tensor::from_data(tensor_data, device))
+}
+
+fn convert_to_f32(data: &[u8], dtype: &str) -> Vec<f32> {
+    match dtype {
+        "BF16" => data
+            .chunks_exact(2)
+            .map(|chunk| {
+                let bytes: [u8; 2] = chunk.try_into().unwrap();
+                bf16_to_f32(u16::from_le_bytes(bytes))
+            })
+            .collect(),
+        "F16" => data
+            .chunks_exact(2)
+            .map(|chunk| {
+                let bytes: [u8; 2] = chunk.try_into().unwrap();
+                f16_to_f32(u16::from_le_bytes(bytes))
+            })
+            .collect(),
+        "F32" => data
+            .chunks_exact(4)
+            .map(|chunk| {
+                let bytes: [u8; 4] = chunk.try_into().unwrap();
+                f32::from_le_bytes(bytes)
+            })
+            .collect(),
+        _ => panic!("Unsupported dtype: {dtype}"),
+    }
+}
+
+fn bf16_to_f32(bf16: u16) -> f32 {
+    let f32_bits = (bf16 as u32) << 16;
+    f32::from_bits(f32_bits)
+}
+
+fn f16_to_f32(f16: u16) -> f32 {
+    let sign = (f16 >> 15) & 1;
+    let exp = (f16 >> 10) & 0x1F;
+    let mant = f16 & 0x3FF;
+
+    if exp == 0 {
+        if mant == 0 {
+            return if sign == 0 { 0.0 } else { -0.0 };
+        }
+        let mut m = mant as u32;
+        let mut e = 0u32;
+        while (m & 0x400) == 0 {
+            m <<= 1;
+            e += 1;
+        }
+        m &= 0x3FF;
+        let f32_exp = 127 - 14 - e;
+        let f32_bits = ((sign as u32) << 31) | (f32_exp << 23) | ((m as u32) << 13);
+        return f32::from_bits(f32_bits);
+    }
+
+    if exp == 0x1F {
+        let f32_bits = ((sign as u32) << 31) | (0xFFu32 << 23) | ((mant as u32) << 13);
+        return f32::from_bits(f32_bits);
+    }
+
+    let f32_exp = (exp as u32) + 127 - 15;
+    let f32_bits = ((sign as u32) << 31) | (f32_exp << 23) | ((mant as u32) << 13);
+    f32::from_bits(f32_bits)
+}
diff --git a/src/inference/mod.rs b/src/inference/mod.rs
new file mode 100644
index 0000000..1b5e619
--- /dev/null
+++ b/src/inference/mod.rs
@@ -0,0 +1,7 @@
+pub mod generation;
+pub mod loader;
+pub mod tokenizer;
+
+pub use generation::{GenerationConfig, generate_with_cache};
+pub use loader::load_safetensors;
+pub use tokenizer::TokenizerWrapper;
diff --git a/src/inference/tokenizer.rs b/src/inference/tokenizer.rs
new file mode 100644
index 0000000..25daf98
--- /dev/null
+++ b/src/inference/tokenizer.rs
@@ -0,0 +1,49 @@
+use tokenizers::Tokenizer;
+
+pub struct TokenizerWrapper {
+    tokenizer: Tokenizer,
+}
+
+impl TokenizerWrapper {
+    pub fn from_file(path: &str) -> anyhow::Result<Self> {
+        let tokenizer = Tokenizer::from_file(path)
+            .map_err(|e| anyhow::anyhow!("Failed to load tokenizer: {}", e))?;
+        Ok(Self { tokenizer })
+    }
+
+    pub fn encode(&self, text: &str, add_special_tokens: bool) -> anyhow::Result<Vec<u32>> {
+        let encoding = self
+            .tokenizer
+            .encode(text, add_special_tokens)
+            .map_err(|e| anyhow::anyhow!("Failed to encode: {}", e))?;
+        Ok(encoding.get_ids().to_vec())
+    }
+
+    pub fn decode(&self, ids: &[u32], skip_special_tokens: bool) -> anyhow::Result<String> {
+        let text = self
+            .tokenizer
+            .decode(ids, skip_special_tokens)
+            .map_err(|e| anyhow::anyhow!("Failed to decode: {}", e))?;
+        Ok(text)
+    }
+
+    pub fn vocab_size(&self) -> usize {
+        self.tokenizer.get_vocab_size(true)
+    }
+
+    /// 应用 MiniCPM5 chat template
+    pub fn apply_chat_template(&self, user_msg: &str, enable_thinking: bool) -> anyhow::Result<Vec<u32>> {
+        let prompt = if enable_thinking {
+            format!(
+                "<s><|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n<think>\n",
+                user_msg
+            )
+        } else {
+            format!(
+                "<s><|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
+                user_msg
+            )
+        };
+        self.encode(&prompt, false)
+    }
+}
diff --git a/src/lib.rs b/src/lib.rs
new file mode 100644
index 0000000..585f6a0
--- /dev/null
+++ b/src/lib.rs
@@ -0,0 +1,9 @@
+pub mod config;
+pub mod exporter;
+pub mod model;
+pub mod inference;
+
+pub use config::{LlamaConfig, EosTokenId};
+pub use model::LlamaForCausalLM;
+pub use inference::{GenerationConfig, generate_with_cache, load_safetensors, TokenizerWrapper};
+pub use exporter::export_model;
diff --git a/src/main.rs b/src/main.rs
new file mode 100644
index 0000000..5fd0ced
--- /dev/null
+++ b/src/main.rs
@@ -0,0 +1,109 @@
+use anyhow::Result;
+use burn::backend::Wgpu;
+use clap::Parser;
+use minicpm_burn_lib::{
+    config::LlamaConfig,
+    inference::{generation, GenerationConfig, load_safetensors, TokenizerWrapper},
+    model::LlamaForCausalLM,
+};
+
+type Backend = Wgpu;
+
+#[derive(Parser, Debug)]
+#[command(name = "minicpm-burn")]
+#[command(version = "0.1.0")]
+enum Command {
+    /// 模型推理
+    Run {
+        /// 输入文本
+        #[arg(short, long)]
+        text: String,
+
+        /// 最大生成 token 数（不指定则无上限）
+        #[arg(short, long)]
+        max_tokens: Option<usize>,
+
+        /// 启用思考模式（temperature=0.9）
+        #[arg(long, default_value_t = false)]
+        think: bool,
+    },
+    /// 转换模型为 Burn MPK 格式
+    Convert {
+        /// 输出目录
+        #[arg(short, long, default_value = "model")]
+        output: String,
+    },
+}
+
+fn main() -> Result<()> {
+    let cmd = Command::parse();
+
+    match cmd {
+        Command::Run { text, max_tokens, think } => run_inference(&text, max_tokens, think),
+        Command::Convert { output } => run_convert(&output),
+    }
+}
+
+fn run_inference(text: &str, max_tokens: Option<usize>, think: bool) -> Result<()> {
+    use std::time::Instant;
+
+    println!("MiniCPM5-1B 推理 [{}]", if think { "思考模式" } else { "直接回答" });
+
+    let device = burn::backend::wgpu::WgpuDevice::default();
+    burn::backend::wgpu::init_setup::<burn::backend::wgpu::graphics::Vulkan>(
+        &device,
+        Default::default(),
+    );
+
+    let config = LlamaConfig::from_json("MiniCPM5-1B/config.json")?;
+    let tokenizer = TokenizerWrapper::from_file("MiniCPM5-1B/tokenizer.json")?;
+    let model = LlamaForCausalLM::<Backend>::new(config.clone(), &device);
+    let model = load_safetensors(model, std::path::Path::new("MiniCPM5-1B/model-00000-of-00001.safetensors"), &device)?;
+
+    let input_ids = tokenizer.apply_chat_template(text, think)?;
+    let gen_config = GenerationConfig {
+        max_new_tokens: max_tokens,
+        temperature: if think { 0.9 } else { 0.7 },
+        top_p: 0.95,
+    };
+
+    let start = Instant::now();
+    let output_ids = generation::generate_with_cache(
+        &model,
+        &input_ids,
+        &gen_config,
+        &config.eos_token_id,
+        &device,
+    );
+    let elapsed = start.elapsed();
+
+    let new_ids = &output_ids[input_ids.len()..];
+    if !new_ids.is_empty() {
+        println!("\n输出: {}", tokenizer.decode(new_ids, true)?);
+    }
+
+    println!("\n耗时: {:.2}s", elapsed.as_secs_f64());
+    println!("速度: {:.2} tokens/s", (output_ids.len() - input_ids.len()) as f64 / elapsed.as_secs_f64());
+
+    Ok(())
+}
+
+fn run_convert(output: &str) -> Result<()> {
+    println!("MiniCPM5-1B 模型转换");
+
+    let device = burn::backend::wgpu::WgpuDevice::default();
+    burn::backend::wgpu::init_setup::<burn::backend::wgpu::graphics::Vulkan>(
+        &device,
+        Default::default(),
+    );
+
+    minicpm_burn_lib::export_model::<Backend>(
+        std::path::Path::new("MiniCPM5-1B/model-00000-of-00001.safetensors"),
+        std::path::Path::new("MiniCPM5-1B/config.json"),
+        std::path::Path::new(output),
+        &device,
+    )?;
+
+    println!("\n转换完成！输出目录: {}", output);
+    Ok(())
+}
diff --git a/src/model/attention.rs b/src/model/attention.rs
new file mode 100644
index 0000000..b2b8ac5
--- /dev/null
+++ b/src/model/attention.rs
@@ -0,0 +1,186 @@
+use burn::module::Module;
+use burn::nn::{Linear, LinearConfig};
+use burn::tensor::activation::softmax;
+use burn::tensor::backend::Backend;
+use burn::tensor::{Float, Tensor};
+
+use super::rope::RoPE;
+
+#[derive(Debug, Clone)]
+pub struct KVCache<B: Backend> {
+    pub k: Tensor<B, 4, Float>,
+    pub v: Tensor<B, 4, Float>,
+}
+
+#[derive(Module, Debug)]
+pub struct Attention<B: Backend> {
+    pub q_proj: Linear<B>,
+    pub k_proj: Linear<B>,
+    pub v_proj: Linear<B>,
+    pub o_proj: Linear<B>,
+    pub n_heads: usize,
+    pub n_kv_heads: usize,
+    pub head_dim: usize,
+}
+
+impl<B: Backend> Attention<B> {
+    pub fn new(
+        hidden_size: usize,
+        n_heads: usize,
+        n_kv_heads: usize,
+        head_dim: usize,
+        device: &B::Device,
+    ) -> Self {
+        let q_proj = LinearConfig::new(hidden_size, n_heads * head_dim).init(device);
+        let k_proj = LinearConfig::new(hidden_size, n_kv_heads * head_dim).init(device);
+        let v_proj = LinearConfig::new(hidden_size, n_kv_heads * head_dim).init(device);
+        let o_proj = LinearConfig::new(n_heads * head_dim, hidden_size).init(device);
+
+        Self {
+            q_proj,
+            k_proj,
+            v_proj,
+            o_proj,
+            n_heads,
+            n_kv_heads,
+            head_dim,
+        }
+    }
+
+    pub fn forward(
+        &self,
+        x: Tensor<B, 3, Float>,
+        rope: &RoPE,
+        mask: Option<Tensor<B, 4, Float>>,
+        offset: usize,
+    ) -> Tensor<B, 3, Float> {
+        let shape = x.shape();
+        let dims: [usize; 3] = shape.dims();
+        let batch = dims[0];
+        let seq_len = dims[1];
+
+        let q = self.q_proj.forward(x.clone());
+        let k = self.k_proj.forward(x.clone());
+        let v = self.v_proj.forward(x);
+
+        let q = q
+            .reshape([batch, seq_len, self.n_heads, self.head_dim])
+            .swap_dims(1, 2);
+        let k = k
+            .reshape([batch, seq_len, self.n_kv_heads, self.head_dim])
+            .swap_dims(1, 2);
+        let v = v
+            .reshape([batch, seq_len, self.n_kv_heads, self.head_dim])
+            .swap_dims(1, 2);
+
+        let q = rope.apply(q, offset);
+        let k = rope.apply(k, offset);
+
+        let k = self.repeat_kv(k);
+        let v = self.repeat_kv(v);
+
+        let scale = (self.head_dim as f64).sqrt() as f32;
+        let scores = q
+            .matmul(k.swap_dims(2, 3))
+            .div_scalar(scale);
+
+        let scores = match mask {
+            Some(mask) => scores + mask,
+            None => scores,
+        };
+
+        let attn = softmax(scores, 3);
+        let output = attn.matmul(v);
+
+        let output = output
+            .swap_dims(1, 2)
+            .reshape([batch, seq_len, self.n_heads * self.head_dim]);
+
+        self.o_proj.forward(output)
+    }
+
+    pub fn forward_with_cache(
+        &self,
+        x: Tensor<B, 3, Float>,
+        rope: &RoPE,
+        mask: Option<Tensor<B, 4, Float>>,
+        offset: usize,
+        cache: Option<&KVCache<B>>,
+    ) -> (Tensor<B, 3, Float>, KVCache<B>) {
+        let shape = x.shape();
+        let dims: [usize; 3] = shape.dims();
+        let batch = dims[0];
+        let seq_len = dims[1];
+
+        let q = self.q_proj.forward(x.clone());
+        let k = self.k_proj.forward(x.clone());
+        let v = self.v_proj.forward(x);
+
+        let q = q
+            .reshape([batch, seq_len, self.n_heads, self.head_dim])
+            .swap_dims(1, 2);
+        let k = k
+            .reshape([batch, seq_len, self.n_kv_heads, self.head_dim])
+            .swap_dims(1, 2);
+        let v = v
+            .reshape([batch, seq_len, self.n_kv_heads, self.head_dim])
+            .swap_dims(1, 2);
+
+        let q = rope.apply(q, offset);
+        let k = rope.apply(k, offset);
+
+        // 合并 KV Cache
+        let (k_full, v_full) = match cache {
+            Some(cache) => {
+                let k_full = Tensor::cat(vec![cache.k.clone(), k], 2);
+                let v_full = Tensor::cat(vec![cache.v.clone(), v], 2);
+                (k_full, v_full)
+            }
+            None => (k.clone(), v.clone()),
+        };
+
+        let new_cache = KVCache {
+            k: k_full.clone(),
+            v: v_full.clone(),
+        };
+
+        let k = self.repeat_kv(k_full);
+        let v = self.repeat_kv(v_full);
+
+        let scale = (self.head_dim as f64).sqrt() as f32;
+        let scores = q
+            .matmul(k.swap_dims(2, 3))
+            .div_scalar(scale);
+
+        let scores = match mask {
+            Some(mask) => scores + mask,
+            None => scores,
+        };
+
+        let attn = softmax(scores, 3);
+        let output = attn.matmul(v);
+
+        let output = output
+            .swap_dims(1, 2)
+            .reshape([batch, seq_len, self.n_heads * self.head_dim]);
+
+        (self.o_proj.forward(output), new_cache)
+    }
+
+    fn repeat_kv(&self, x: Tensor<B, 4, Float>) -> Tensor<B, 4, Float> {
+        let n_rep = self.n_heads / self.n_kv_heads;
+        if n_rep == 1 {
+            return x;
+        }
+
+        let shape = x.shape();
+        let dims: [usize; 4] = shape.dims();
+        let batch = dims[0];
+        let kv_heads = dims[1];
+        let seq_len = dims[2];
+        let head_dim = dims[3];
+
+        let x = x.reshape([batch, kv_heads, 1, seq_len, head_dim]).expand([batch, kv_heads, n_rep, seq_len, head_dim]);
+        x.reshape([batch, kv_heads * n_rep, seq_len, head_dim])
+    }
+}
\ No newline at end of file
diff --git a/src/model/decoder.rs b/src/model/decoder.rs
new file mode 100644
index 0000000..d6c318c
--- /dev/null
+++ b/src/model/decoder.rs
@@ -0,0 +1,81 @@
+use burn::module::Module;
+use burn::tensor::backend::Backend;
+use burn::tensor::{Float, Tensor};
+
+use super::attention::{Attention, KVCache};
+use super::ffn::FeedForward;
+use super::norm::RmsNorm;
+use super::rope::RoPE;
+
+#[derive(Module, Debug)]
+pub struct DecoderLayer<B: Backend> {
+    pub self_attn: Attention<B>,
+    pub mlp: FeedForward<B>,
+    pub input_layernorm: RmsNorm<B>,
+    pub post_attention_layernorm: RmsNorm<B>,
+}
+
+impl<B: Backend> DecoderLayer<B> {
+    pub fn new(
+        hidden_size: usize,
+        intermediate_size: usize,
+        n_heads: usize,
+        n_kv_heads: usize,
+        head_dim: usize,
+        rms_norm_eps: f64,
+        device: &B::Device,
+    ) -> Self {
+        let self_attn = Attention::new(hidden_size, n_heads, n_kv_heads, head_dim, device);
+        let mlp = FeedForward::new(hidden_size, intermediate_size, device);
+        let input_layernorm = RmsNorm::new(hidden_size, rms_norm_eps, device);
+        let post_attention_layernorm = RmsNorm::new(hidden_size, rms_norm_eps, device);
+
+        Self {
+            self_attn,
+            mlp,
+            input_layernorm,
+            post_attention_layernorm,
+        }
+    }
+
+    pub fn forward(
+        &self,
+        x: Tensor<B, 3, Float>,
+        rope: &RoPE,
+        mask: Option<Tensor<B, 4, Float>>,
+        offset: usize,
+    ) -> Tensor<B, 3, Float> {
+        let residual = x.clone();
+        let x = self.input_layernorm.forward(x);
+        let x = self.self_attn.forward(x, rope, mask, offset);
+        let x = residual + x;
+
+        let residual = x.clone();
+        let x = self.post_attention_layernorm.forward(x);
+        let x = self.mlp.forward(x);
+        let x = residual + x;
+
+        x
+    }
+
+    pub fn forward_with_cache(
+        &self,
+        x: Tensor<B, 3, Float>,
+        rope: &RoPE,
+        mask: Option<Tensor<B, 4, Float>>,
+        offset: usize,
+        cache: Option<&KVCache<B>>,
+    ) -> (Tensor<B, 3, Float>, KVCache<B>) {
+        let residual = x.clone();
+        let x = self.input_layernorm.forward(x);
+        let (x, kv_cache) = self.self_attn.forward_with_cache(x, rope, mask, offset, cache);
+        let x = residual + x;
+
+        let residual = x.clone();
+        let x = self.post_attention_layernorm.forward(x);
+        let x = self.mlp.forward(x);
+        let x = residual + x;
+
+        (x, kv_cache)
+    }
+}
\ No newline at end of file
diff --git a/src/model/ffn.rs b/src/model/ffn.rs
new file mode 100644
index 0000000..5f2f0d1
--- /dev/null
+++ b/src/model/ffn.rs
@@ -0,0 +1,33 @@
+use burn::module::Module;
+use burn::nn::{Linear, LinearConfig};
+use burn::tensor::backend::Backend;
+use burn::tensor::activation::silu;
+use burn::tensor::{Float, Tensor};
+
+#[derive(Module, Debug)]
+pub struct FeedForward<B: Backend> {
+    pub gate_proj: Linear<B>,
+    pub up_proj: Linear<B>,
+    pub down_proj: Linear<B>,
+}
+
+impl<B: Backend> FeedForward<B> {
+    pub fn new(hidden_size: usize, intermediate_size: usize, device: &B::Device) -> Self {
+        let gate_proj = LinearConfig::new(hidden_size, intermediate_size).init(device);
+        let up_proj = LinearConfig::new(hidden_size, intermediate_size).init(device);
+        let down_proj = LinearConfig::new(intermediate_size, hidden_size).init(device);
+
+        Self {
+            gate_proj,
+            up_proj,
+            down_proj,
+        }
+    }
+
+    pub fn forward(&self, x: Tensor<B, 3, Float>) -> Tensor<B, 3, Float> {
+        let gate = self.gate_proj.forward(x.clone());
+        let up = self.up_proj.forward(x);
+        let activated = silu(gate) * up;
+        self.down_proj.forward(activated)
+    }
+}
\ No newline at end of file
diff --git a/src/model/mod.rs b/src/model/mod.rs
new file mode 100644
index 0000000..5b49a4d
--- /dev/null
+++ b/src/model/mod.rs
@@ -0,0 +1,9 @@
+pub mod attention;
+pub mod decoder;
+pub mod ffn;
+pub mod model;
+pub mod norm;
+pub mod rope;
+
+pub use attention::KVCache;
+pub use model::{LlamaForCausalLM, LlamaKVCache};
\ No newline at end of file
diff --git a/src/model/model.rs b/src/model/model.rs
new file mode 100644
index 0000000..16efe5e
--- /dev/null
+++ b/src/model/model.rs
@@ -0,0 +1,164 @@
+use burn::module::Module;
+use burn::nn::{Embedding, EmbeddingConfig, Linear, LinearConfig};
+use burn::tensor::backend::Backend;
+use burn::tensor::{Float, Int, Tensor};
+
+use super::attention::KVCache;
+use crate::config::LlamaConfig;
+use super::decoder::DecoderLayer;
+use super::norm::RmsNorm;
+use super::rope::RoPE;
+
+#[derive(Debug, Clone)]
+pub struct LlamaKVCache<B: Backend> {
+    pub layers: Vec<KVCache<B>>,
+}
+
+impl<B: Backend> LlamaKVCache<B> {
+    pub fn new(num_layers: usize) -> Self {
+        Self {
+            layers: Vec::with_capacity(num_layers),
+        }
+    }
+}
+
+#[derive(Module, Debug)]
+pub struct LlamaModel<B: Backend> {
+    pub embed_tokens: Embedding<B>,
+    pub layers: Vec<DecoderLayer<B>>,
+    pub norm: RmsNorm<B>,
+    pub rope: RoPE,
+}
+
+#[derive(Module, Debug)]
+pub struct LlamaForCausalLM<B: Backend> {
+    pub model: LlamaModel<B>,
+    pub lm_head: Linear<B>,
+    pub config: LlamaConfig,
+}
+
+impl<B: Backend> LlamaForCausalLM<B> {
+    pub fn new(config: LlamaConfig, device: &B::Device) -> Self {
+        let head_dim = config.head_dim();
+        let rope = RoPE::new(head_dim, config.rope_theta, config.max_position_embeddings);
+
+        let embed_tokens =
+            EmbeddingConfig::new(config.vocab_size, config.hidden_size).init(device);
+
+        let mut layers = Vec::with_capacity(config.num_hidden_layers);
+        for _ in 0..config.num_hidden_layers {
+            layers.push(DecoderLayer::new(
+                config.hidden_size,
+                config.intermediate_size,
+                config.num_attention_heads,
+                config.num_key_value_heads,
+                head_dim,
+                config.rms_norm_eps,
+                device,
+            ));
+        }
+
+        let norm = RmsNorm::new(config.hidden_size, config.rms_norm_eps, device);
+
+        let lm_head = LinearConfig::new(config.hidden_size, config.vocab_size).init(device);
+
+        let model = LlamaModel {
+            embed_tokens,
+            layers,
+            norm,
+            rope,
+        };
+
+        Self {
+            model,
+            lm_head,
+            config,
+        }
+    }
+
+    pub fn forward(&self, input_ids: Tensor<B, 2, Int>) -> Tensor<B, 3, Float> {
+        let shape = input_ids.shape();
+        let dims: [usize; 2] = shape.dims();
+        let seq_len = dims[1];
+
+        let mask = if seq_len > 1 {
+            Some(self.make_causal_mask(seq_len, &input_ids.device()))
+        } else {
+            None
+        };
+
+        let x = self.model.embed_tokens.forward(input_ids);
+
+        let mut x = x;
+        for layer in &self.model.layers {
+            x = layer.forward(x, &self.model.rope, mask.clone(), 0);
+        }
+
+        let x = self.model.norm.forward(x);
+
+        let logits = self.lm_head.forward(x);
+
+        logits
+    }
+
+    pub fn forward_with_cache(
+        &self,
+        input_ids: Tensor<B, 2, Int>,
+        cache: Option<&LlamaKVCache<B>>,
+    ) -> (Tensor<B, 3, Float>, LlamaKVCache<B>) {
+        let shape = input_ids.shape();
+        let dims: [usize; 2] = shape.dims();
+        let seq_len = dims[1];
+
+        let offset = match &cache {
+            Some(cache) => {
+                let k_dims: [usize; 4] = cache.layers[0].k.shape().dims();
+                k_dims[2]
+            }
+            None => 0,
+        };
+
+        let mask = if seq_len > 1 {
+            Some(self.make_causal_mask(seq_len, &input_ids.device()))
+        } else {
+            None
+        };
+
+        let x = self.model.embed_tokens.forward(input_ids);
+
+        let mut x = x;
+        let mut new_cache = LlamaKVCache::new(self.config.num_hidden_layers);
+
+        for (i, layer) in self.model.layers.iter().enumerate() {
+            let layer_cache = cache.as_ref().map(|c| &c.layers[i]);
+            let (output, kv_cache) = layer.forward_with_cache(
+                x,
+                &self.model.rope,
+                mask.clone(),
+                offset,
+                layer_cache,
+            );
+            x = output;
+            new_cache.layers.push(kv_cache);
+        }
+
+        let x = self.model.norm.forward(x);
+
+        let logits = self.lm_head.forward(x);
+
+        (logits, new_cache)
+    }
+
+    fn make_causal_mask(&self, seq_len: usize, device: &B::Device) -> Tensor<B, 4, Float> {
+        let mut mask = vec![0.0f32; seq_len * seq_len];
+        for i in 0..seq_len {
+            for j in 0..seq_len {
+                if j > i {
+                    mask[i * seq_len + j] = f32::NEG_INFINITY;
+                }
+            }
+        }
+        Tensor::<B, 1, Float>::from_floats(mask.as_slice(), device)
+            .reshape([1, 1, seq_len, seq_len])
+    }
+}
\ No newline at end of file
diff --git a/src/model/norm.rs b/src/model/norm.rs
new file mode 100644
index 0000000..e69eb0c
--- /dev/null
+++ b/src/model/norm.rs
@@ -0,0 +1,29 @@
+use burn::module::{Module, Param};
+use burn::nn::Initializer;
+use burn::tensor::backend::Backend;
+use burn::tensor::{Float, Tensor};
+
+#[derive(Module, Debug)]
+pub struct RmsNorm<B: Backend> {
+    pub weight: Param<Tensor<B, 1, Float>>,
+    pub eps: f64,
+}
+
+impl<B: Backend> RmsNorm<B> {
+    pub fn new(dim: usize, eps: f64, device: &B::Device) -> Self {
+        let weight = Initializer::Ones.init([dim], device);
+        Self {
+            weight,
+            eps,
+        }
+    }
+
+    pub fn forward<const D: usize>(&self, x: Tensor<B, D, Float>) -> Tensor<B, D, Float> {
+        let variance = x.clone().powf_scalar(2.0).mean_dim(D - 1);
+        let rms = (variance + self.eps).sqrt();
+        let x_norm = x / rms;
+
+        let weight = self.weight.val().clone().unsqueeze();
+        x_norm * weight
+    }
+}
\ No newline at end of file
diff --git a/src/model/rope.rs b/src/model/rope.rs
new file mode 100644
index 0000000..d964903
--- /dev/null
+++ b/src/model/rope.rs
@@ -0,0 +1,83 @@
+use burn::tensor::backend::Backend;
+use burn::tensor::{Float, Tensor};
+
+#[derive(Debug, Clone)]
+pub struct RoPE {
+    pub dim: usize,
+    pub theta: f64,
+    pub max_seq_len: usize,
+    cos_cache: Vec<f32>,
+    sin_cache: Vec<f32>,
+    half_dim: usize,
+}
+
+impl RoPE {
+    pub fn new(dim: usize, theta: f64, max_seq_len: usize) -> Self {
+        let half_dim = dim / 2;
+
+        let mut inv_freqs = Vec::with_capacity(half_dim);
+        for i in 0..half_dim {
+            let freq = 1.0 / (theta.powf((2.0 * i as f64) / dim as f64));
+            inv_freqs.push(freq as f32);
+        }
+
+        let mut cos_cache = Vec::with_capacity(max_seq_len * half_dim);
+        let mut sin_cache = Vec::with_capacity(max_seq_len * half_dim);
+
+        for pos in 0..max_seq_len {
+            for &freq in &inv_freqs {
+                let angle = pos as f32 * freq;
+                cos_cache.push(angle.cos());
+                sin_cache.push(angle.sin());
+            }
+        }
+
+        Self {
+            dim,
+            theta,
+            max_seq_len,
+            cos_cache,
+            sin_cache,
+            half_dim,
+        }
+    }
+
+    pub fn get_cache(&self, offset: usize, len: usize) -> (Vec<f32>, Vec<f32>) {
+        let cos = self.cos_cache[offset * self.half_dim..(offset + len) * self.half_dim].to_vec();
+        let sin = self.sin_cache[offset * self.half_dim..(offset + len) * self.half_dim].to_vec();
+        (cos, sin)
+    }
+
+    pub fn apply<B: Backend>(
+        &self,
+        x: Tensor<B, 4, Float>,
+        offset: usize,
+    ) -> Tensor<B, 4, Float> {
+        let device = x.device();
+        let shape = x.shape();
+        let dims: [usize; 4] = shape.dims();
+        let batch = dims[0];
+        let heads = dims[1];
+        let seq_len = dims[2];
+        let dim = dims[3];
+        let half_dim = self.half_dim;
+
+        let cos_vals = &self.cos_cache[offset * half_dim..(offset + seq_len) * half_dim];
+        let sin_vals = &self.sin_cache[offset * half_dim..(offset + seq_len) * half_dim];
+
+        let cos = Tensor::<B, 1, Float>::from_floats(cos_vals, &device)
+            .reshape([1, 1, seq_len, half_dim])
+            .expand([batch, heads, seq_len, half_dim]);
+        let sin = Tensor::<B, 1, Float>::from_floats(sin_vals, &device)
+            .reshape([1, 1, seq_len, half_dim])
+            .expand([batch, heads, seq_len, half_dim]);
+
+        let x1 = x.clone().slice([0..batch, 0..heads, 0..seq_len, 0..half_dim]);
+        let x2 = x.slice([0..batch, 0..heads, 0..seq_len, half_dim..dim]);
+
+        let x1_rot = x1.clone() * cos.clone() - x2.clone() * sin.clone();
+        let x2_rot = x2 * cos + x1 * sin;
+
+        Tensor::cat(vec![x1_rot, x2_rot], 3)
+    }
+}
\ No newline at end of file